A new framework for high-resolution pedestrian data processing using rule-based algorithms and real-time alarm systems

Michael Moos, Basil Vitins, Mirwais Tayebi, Lukas Gamper, Julia Wysling, Uri Schtalheim


Pedestrian flows and densities have increased in recent years within transport-related public facilities such as train stations, as well as in private buildings such as shopping centers, event halls or convention centers. Increasing flows and high densities often raise comfort, safety, operational and delay issues; and therefore, require pedestrian flow optimization, intervention or even revised regulation. Recent technological advances enhanced pedestrian sensing; however, they disregard adaptive data capture, processing, and strategic communication within reasonable time, or real-time, such as tactic occupancy or density alarms trigger rules. Content of this research is twofold. First, new data capturing and processing advances of recent technological developments are combined in an integral software and hardware-based framework. Second, applied methods highlight projects and experiences on both pedestrian research and on existing and operating pedestrian facilities. Based on the described, two-sided approach, proposed framework is able to fulfil high safety and comfort standards of facilities such as train stations, retail facilities or event halls. In this research, past semi-automatic video analysis processing of pedestrian behavioral studies is replaced with combined sensor and data processing system within proposed framework. In train stations of major operators, real-time pedestrian observation increases safety levels on station platforms. Tactic algorithms and alarm trigger schemes enable on-time surveillance, e.g. at overcrowded floor levels in shopping centers for escalator or door closure. Sensor data is used to train models for underpass pedestrian flow regarding path choice and fundamental diagram. In retail, queue length, trajectory analysis and floor occupancy are determined for economic, comfort as well as safety evaluation. Using trajectory classification, movement and dwell time is analyzed for staff and visitors separately (see Figure 1).


video analysis; realtime monitoring; alarming; model calibration; trajectory classification

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J. Thurau, J. Van den Heuvel, M. Ofwegen, N. Keusen and S. Hoogendoorn „Influence of pedestrian density on the use of the danger

zone at platforms of train stations”, Conference on Traffic and Granular Flow, Washington DC, July 2017.

D. Helbing and A. Johansson “Pedestrian, Crowd and Evacuation Dynamics”, Encyclopedia of Complexity and Systems Science, vol. 16,

pp. 6476-6495, 2010.

T. Kretz, “Pedestrian traffic: on the quickest path”, J.Stat. Mech. Theor. Exp., vol. 03, pp. 1-13, 2009.

A. Johansson, D. Helbing and P.K. Shukla, “Specification of the social force pedestrian model by evolutionary adjustment to video tracking

data”, Adv. Compl. Syst., vol. 10, pp. 271-288.

S. Buchmueller and U. Weidmann, Parameters of pedestrians, pedestrian traffic and walking facilities, Schriftenreihe, vol. 132, ETH

Zurich, 2007.

D. Duives, W. Daamen and S. Hoogendoorn “Quantification of the level of crowdedness for pedestrian movements”, Physica A Stat.

Mech. Appl., vol. 427, June 2015.

DOI: http://dx.doi.org/10.17815/CD.2020.99

Copyright (c) 2020 Michael Moos, Basil Vitins, Mirwais Tayebi, Lukas Gamper, Julia Wysling, Uri Schtalheim

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